Intelligent Materials and Catalysis (IMC) is an international, peer-reviewed, open-access journal dedicated to advancing the science and engineering of next-generation materials and catalytic systems enhanced by artificial intelligence (AI), machine learning (ML), and data-driven approaches. The journal provides a high-impact platform for original research, critical reviews, and visionary perspectives that bridge materials design, catalytic innovation, automation, and computational intelligence.
IMC aims to catalyze innovation at the interface of materials science, chemistry, chemical engineering, computational science, and data analytics, fostering a new paradigm of intelligent discovery and sustainable transformation.
Scope
The journal welcomes high-quality contributions in (but not limited to) the following areas:
AI-Driven Materials Discovery and Design
- Machine learning and deep learning for materials prediction, synthesis planning, and performance optimization
- Automated and self-driving laboratories for materials and catalysis research
- Digital twins and computational frameworks for accelerated innovation
Intelligent Catalytic Systems
- Adaptive, responsive, and self-optimizing catalysts
- Integration of AI in heterogeneous, homogeneous, and biocatalysis
- Data-driven kinetic modeling and reaction network analysis
- Catalysts for sustainable processes, energy conversion, and environmental remediation
Smart and Functional Materials
- Stimuli-responsive, self-healing, or programmable materials
- Hybrid nanomaterials and multifunctional composites
- AI-assisted synthesis, processing, and property prediction
Sustainable and Green Technologies
- Catalytic processes for carbon capture, utilization, and storage (CCUS)
- Photocatalysis, electrocatalysis, and mechanocatalysis for renewable energy
- Waste valorization and circular materials economy
Computational and Theoretical Catalysis
- Multiscale modeling, quantum chemistry, and reactive simulations
- Explainable AI and interpretable models for reaction mechanisms
- Data infrastructures, ontologies, and knowledge graphs in catalysis
Emerging Interfaces
- Integration of robotics, automation, and AI for experimental workflows
- In situ and operando data analysis using AI algorithms
- Autonomous systems for catalyst development and materials optimization
Mission and Vision
IMC seeks to foster transdisciplinary collaboration and promote data-driven intelligence as a transformative tool for materials and catalysis. By connecting traditional experimental sciences with computational and AI technologies, the journal aims to accelerate discovery, enhance sustainability, and redefine innovation pathways in the materials and catalysis domains.
The journal welcomes original research articles, reviews, perspectives, short communications, and data-centric studies.
